Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion

Occipital connectivity networks mediate the neural effects of childhood maltreatment on depressive symptoms in major depressive disorder

BACKGROUND

Childhood maltreatment (CM) is a well-established risk factor for major depressive disorder (MDD). The neural mechanisms linking childhood maltreatment experiences to changes in brain functional networks and the onset of depression are not fully understood.

METHODS

In this study, we enrolled 66 patients with MDD and 31 healthy controls who underwent resting-state fMRI scans and neuropsychological assessments. We employed multivariate linear regression to examine the neural associations of CM and depression, specifically focusing on the bilateral occipital functional connectivity (OFC) networks relevant to MDD. Subsequently, a two-step mediation analysis was conducted to assess whether the OFC network mediated the relationship between CM experiences and the severity of depression.

RESULTS

Our study showed that patients with MDD exhibited reduced OFC strength, particularly in the occipito-temporal, parietal, and premotor regions. These reductions were negatively correlated with CM scores and the severity of depression. Notably, the overlapping regions in the bilateral OFC networks, affected by both CM experiences and depressive severity, were primarily observed in the bilateral cuneus, left angular and calcarine, as well as the right middle frontal cortex and superior parietal cortex. Furthermore, the altered strengths of the OFC networks were identified as positive mediators of the impact of CM history on depression symptoms in patients with MDD.

CONCLUSION

We have demonstrated that early exposure to CM may increase vulnerability to depression by influencing the brain's network. These findings provide new insights into understanding the pathological mechanism underlying depressive symptoms induced by CM.

Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system

PURPOSE

MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac.

METHODS AND MATERIALS

The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes.

RESULTS

The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared.

CONCLUSION

Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.

Quality control in resting-state fMRI: the benefits of visual inspection

Background

A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or "scrubbing" volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection.

Results

The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts.

Conclusion

Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis.

Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate

Reliability and robustness of resting state functional connectivity MRI (rs-fcMRI) relies, in part, on minimizing the influence of head motion on measured brain signals. The confounding effects of head motion on functional connectivity have been extensively studied in adults, but its impact on newborn brain connectivity remains unexplored. Here, using a large newborn data set consisting of 159 rs-fcMRI scans acquired in the Developing Brain Institute at Children's National Hospital and 416 scans from The Developing Human Connectome Project (dHCP), we systematically investigated associations between head motion and rs-fcMRI. Head motion during the scan significantly affected connectivity at sensory-related networks and default mode networks, and at the whole brain scale; the direction of motion effects varied across the whole brain. Comparing high- versus low-head motion groups suggested that head motion can impact connectivity estimates across the whole brain. Censoring of high-motion volumes using frame-wise displacement significantly reduced the confounding effects of head motion on neonatal rs-fcMRI. Lastly, in the dHCP data set, we demonstrated similar persistent associations between head motion and network connectivity despite implementing a standard denoising strategy. Collectively, our results highlight the importance of using rigorous head motion correction in preprocessing neonatal rs-fcMRI to yield reliable estimates of brain activity.

Camera-based Prospective Motion Correction in Paediatric Epilepsy Patients Enables EEG-fMRI Localization Even in High-motion States

BACKGROUND

EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction.

METHODS

Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other.

RESULTS

Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity < 1.5 mm/s) and showed high inter- and intra-individual variability. Comparing motion measured by the PMC camera and the (uncorrected residual) motion detected by realignment of fMRI images, there was a five-fold reduction in motion from its prospective correction. Retrospective EEG correction using both standard approaches and REEGMAS allowed the visualization and identification of physiological noise and epileptiform discharges. Seven of 10 children had significant maps, which were concordant with the clinical EZ hypothesis in 6 of these 7.

CONCLUSION

To our knowledge this is the first application of camera-based PMC for MRI in a pediatric clinical setting. Despite large amount of movement PMC in combination with retrospective EEG correction recovered data and obtained clinically meaningful results during high levels of subject motion. Practical limitations may currently limit the widespread use of this technology.

Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis

Introduction

Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes.

Methods

We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS.

Results

No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation.

Discussion

Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.

Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI

In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.

Real-time and Recursive Estimators for Functional MRI Quality Assessment

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion

The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.

Advances in resting state fMRI acquisitions for functional connectomics

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

Pitfalls on PET/MRI

A decade of PET/MRI clinical imaging has passed and many of the pitfalls are similar to those on earlier studies. However, techniques to overcome them have emerged and continue to develop. Although clinically significant lung nodules are demonstrable, smaller nodules may be detected using ultrashort/zero echo-time (TE) lung MRI. Fast reconstruction ultrashort TE sequences have also been used to achieve high-resolution lung MRI even with free-breathing. The introduction and improvement of time-of-flight scanners and increasing the axial length of the PET detector arrays have more than doubled the sensitivity of the PET part of the system. MRI for attenuation correction has provided many potential pitfalls, including misclassification of tissue classes based on MRI information for attenuation correction. Although the use of short echo times have helped to address these pitfalls, one of the most exciting developments has been the use of deep learning algorithms and computational neural networks to rapidly provide soft tissue, fat, bone and air information for the attenuation correction as a supplement to the attenuation correction information from fat-water imaging. Challenges with motion correction, particularly respiratory and cardiac remain but are being addressed with respiratory monitors and using PET data. In order to address truncation artefacts, the system manufacturers have developed methods to extend the MR field-of-view for the purpose of the attenuation and scatter corrections. General pitfalls like stitching of body sections for individual studies, optimum delivery of images for viewing and reporting, and resource implications for the sheer volume of data generated remain Methods to overcome these pitfalls serve as a strong foundation for the future of PET/MRI. Advances in the underlying technology with significant evolution in hard-ware and software and the exiting developments in use of deep learning algorithms and computational neural networks will drive the next decade of PET/MRI imaging.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.